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Autor*in

  • Kowarik, Stefan (18)
  • Pithan, L. (5)
  • Liehr, Sascha (4)
  • Beyer, P. (2)
  • Bornemann-Pfeiffer, Martin (2)
  • Chruscicki, Sebastian (2)
  • Duva, G. (2)
  • Gerlach, A. (2)
  • Hinderhofer, A. (2)
  • Kern, Simon (2)
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Erscheinungsjahr

  • 2020 (4)
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  • Artificial neural networks (4)
  • Distributed acoustic sensing (3)
  • X-ray reflectivity (3)
  • Artificial Neural Networks (2)
  • Automation (2)
  • Distributed fiber optic sensing (2)
  • Faseroptische Sensorik (2)
  • Fiber optic sensing (2)
  • Online NMR Spectroscopy (2)
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Organisationseinheit der BAM

  • 8 Zerstörungsfreie Prüfung (18)
  • 8.6 Faseroptische Sensorik (18)
  • 1 Analytische Chemie; Referenzmaterialien (2)
  • 1.4 Prozessanalytik (2)
  • 8.0 Abteilungsleitung und andere (1)

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Fiber Optic sensing @BAM (2017)
Kowarik, Stefan
I will discuss fiber optic sensing principles at BAM. Overlapping areas of interes between our group and the Institut für Angewandte Photonik will be discussed.
Neuartiger faseroptischer Temperatursensor basierend auf thermoresponsiven Polymeren (2017)
Kowarik, Stefan
Wir zeigen Resultate für einen faseroptischen Temperatursensor, der auf der temperaturabhängigen Eintrübung einer wässrigen Polymerlösung beruht. Da der Sensor auf dem Phasenübergang der spinodalen Entmischung bei fester Temepratur beruht, kann sich der Sensor selbst kalibrieren und daher für Anwendungen zur absoluten Temperaturmessung eingesetzt werden.
Triggering molecular scale processes with light: from photoalignment of molecules to amplification in molecular switches (2017)
Kowarik, Stefan
We present recent results on photoalignment and cooperative molecular switching in thin films and nanofibers.
Thin-Film Texture and Optical Properties of Donor/Acceptor Complexes. Diindenoperylene/F6TCNNQ vs Alpha-Sexithiophene/ F6TCNNQ (2018)
Duva, G. ; Pithan, L. ; Zeiser, C. ; Reisz, B. ; Dieterle, J. ; Hofferberth, B. ; Beyer, P. ; Bogula, L. ; Opitz, A. ; Kowarik, Stefan ; Hinderhofer, A. ; Gerlach, A. ; Schreiber, F.
In this work, two novel donor/acceptor (D/A) complexes, namely, diindenoperylene (DIP)/1,3,4,5,7,8-hexafluoro-tetracyanonaphthoquinodimethane (F6TCNNQ) and alpha-sexithiophene (6T)/F6TCNNQ, are studied. The D/A complexes segregate in form of π−π stacked D/A cocrystals and can be observed by X-ray scattering. The different conformational degrees of freedom of the donor molecules, respectively, seem to affect the thin-film crystalline texture and composition of the D/A mixtures significantly. In equimolar mixtures, for DIP/F6TCNNQ, the crystallites are mostly uniaxially oriented and homogeneous, whereas for 6T/F6TCNNQ, a mostly 3D (isotropic) orientation of the crystallites and coexistence of domains of pristine compounds and D/A complex, respectively, are observed. Using optical absorption spectroscopy, we observe for each of the two mixed systems a set of new, strong transitions located in the near-IR range below the gap of the pristine compounds: such transitions are related to charge-transfer (CT) interactions between donor and acceptor. The optical anisotropy of domains of the D/A complexes with associated new electronic states is studied by ellipsometry. We infer that the CT-related transition dipole moment is perpendicular to the respective π-conjugated planes in the D/A complex.
Molecular structure of the substrate-induced thin-film phase of tetracene (2018)
Kowarik, Stefan ; Pithan, L. ; Nabok, D. ; Cocchi, C. ; Beyer, P. ; Duva, G. ; Simbrunner, J. ; Rawle, J. ; Nicklin, C. ; Schäfer, P. ; Draxl, C. ; Schreiber, F.
We present a combined experimental and theoretical study to solve the unit-cell and molecular arrangement of the tetracene thin film (TF) phase. TF phases, also known as substrate induced phases (SIP), are polymorphs that exist at interfaces and decisively impact the functionality of organic thin films, e.g., in a transistor channel, but also change the optical spectra due to the different molecular packing. As SIPs only exist in textured ultrathin films, their structure determination remains challenging compared to bulk materials. Here, we use grazing incidence Xray diffraction and atomistic simulations to extract the TF unit-cell parameters of tetracene together with the atomic positions within the unit-cell.
Training Data of Quantitative Online NMR Spectroscopy for Artificial Neural Networks (2020)
Kern, Simon ; Liehr, Sascha ; Wander, Lukas ; Bornemann-Pfeiffer, Martin ; Müller, S. ; Maiwald, Michael ; Kowarik, Stefan
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi (“pure component spectra dataset”) Xii (“spectral model dataset”) Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.
Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis (2020)
Kowarik, Stefan ; Hussels, Maria-Teresa ; Chruscicki, Sebastian ; Münzenberger, Sven ; Lämmerhirt, A. ; Pohl, P. ; Schubert, M.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.
Artificial neural networks for quantitative online NMR spectroscopy (2020)
Kern, Simon ; Liehr, Sascha ; Wander, Lukas ; Bornemann-Pfeiffer, Martin ; Müller, S. ; Maiwald, Michael ; Kowarik, Stefan
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Train monitoring using distributed fiber optic acoustic sensing (2020)
Kowarik, Stefan ; Hicke, Konstantin ; Chruscicki, Sebastian ; Schukar, Marcus ; Breithaupt, Mathias ; Lämmerhirt, A. ; Pohl, P. ; Schubert, M.
We use distributed acoustic sensing to determine the velocity of trains from train vibration patterns using artificial neural network and conventional algorithms. The velocity uncertainty depends on track conditions, train type and velocity.
Multiple timescales in the photoswitching kinetics of crystalline thin films of azobenzene-trimers (2017)
Kowarik, Stefan ; Weber, C. ; Pithan, L. ; Zykov, A. ; Bommel, S. ; Carla, F. ; Felici, R. ; Knie, C. ; Bléger, D.
Functional materials that exhibit photoinduced structural phase transitions are highly interesting for applications in optomechanics and mechanochemistry. It is, however, still not fully understood how photochemical reactions, which are often accompanied by molecular motion, proceed in confined and crystalline environments. Here we show that thin films of azobenzene trimers exhibit high structural order and determine the crystallographic unit cell. We demonstrate that thin film can be switched partially reversibly between a crystalline and an amorphous phase. The time constant of the photoinduced amorphisation as measured with real-time x-ray diffraction ($\approx $ 220 s) lies between the two time constants (120 s and 2870 s) of the ensemble photoisomerisation processes that are measured via optical spectroscopy. Our observation of a photoinduced shrinking of the crystalline domains indicates a cascading process, in which photoisomerisation starts at the surface of the thin film and propagates deeper into the crystalline layer by introducing disorder and generating free volume. This finding is important for the rapidly evolving research field of photoresponsive thin films and smart crystalline materials in general.
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